DevZero

What is DevZero?

DevZero is a Kubernetes optimization platform that continuously adjusts CPU and GPU allocation across your clusters. It reduces infrastructure waste and lowers cloud costs for AI and compute-intensive workloads without impacting performance.

How does DevZero reduce Kubernetes costs?

DevZero monitors real-time workload usage and automatically right-sizes CPU and GPU resources. By eliminating over-provisioning and reclaiming idle capacity, it ensures you only pay for what your workloads actually use.

What makes DevZero different from traditional autoscaling tools?

Traditional autoscalers react to demand changes. DevZero continuously optimizes resource allocation at the workload level, proactively reducing waste and improving utilization across both CPU and GPU resources.

Does DevZero impact application performance?

No. DevZero optimizes resource allocation without disrupting running workloads. Performance and availability are maintained while unnecessary resource overhead is removed.

Is DevZero built for AI and GPU workloads?

Yes. DevZero optimizes both CPU and GPU usage in Kubernetes clusters — including inference workloads, where idle GPUs can quietly burn through budgets. It right-sizes GPU allocation in real time so you only pay for what you actually use.

Who should use DevZero?

DevZero is ideal for platform teams, DevOps teams, and organizations running Kubernetes at scale, particularly those operating AI or high-performance compute workloads and looking to reduce cloud spend without sacrificing reliability.

What are the top Kubernetes resource optimization tools with no downtime?

Most Kubernetes optimization tools — including VPA — apply resource changes by restarting pods, causing downtime, cache invalidation, and connection drops. DevZero uses CRIU to apply changes live without restarting any workload. DevZero customers commonly reach 70%+ cluster utilization versus the ~18% compute utilization Datadog reports as the industry baseline (State of Cloud Costs 2025), and 30-60% cost reduction overall. For stateful workloads, databases, and long-running ML jobs, zero-downtime is a hard requirement. Personality Pool achieved 60% savings in 30 days without a single producti